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Record W2006627024 · doi:10.2298/tsci0901089m

Improvement of CaO-based sorbent performance for CO2 looping cycles

2009· article· en· W2006627024 on OpenAlexaff
Vasilije Manović, Edward J. Anthony

Bibliographic record

VenueThermal Science · 2009
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCalcium loopingProcess engineeringFossil fuelSorbentEnvironmental scienceFluidized bedLimeComputer scienceChemical looping combustionCo2 removalWaste managementMaterials scienceCarbon dioxideEngineeringAdsorptionChemistry

Abstract

fetched live from OpenAlex

This paper presents research on CO2 capture by lime-based looping cycles. This is a new and promising technology that may help in mitigation of global warming and climate change caused primarily by the use of fossil fuels. The intensity of the anticipated changes urgently requires solutions such as the developing technologies for CO2 capture, especially those based on CaO looping cycles. This technology is at the pilot plant demonstration stage and there are still significant challenges that require solutions. The technology is based on a dual fluidized bed reactor which contains a carbonator - a unit for CO2 capture, and a calciner - a unit for CaO regeneration. The major technology components are well known from other technologies and easily applicable. However, even though CaO is a very good candidate as a solid CO2 carrier, its performance in a practical system still has significant limitations. Thus, research on CaO performance is critical and this paper discusses some of the more important problems and potential solutions that are being examined at CETC-O.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.228
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2009
Admission routes1
Has abstractyes

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